Grade Selection and Blending to Optimize Cost and Quality

In many chemical process applications, a large mix of products is produced by blending them from a much smaller set of basic grades. The basic grades themselves are typically produced on the same process equipment and inventoried in batches. Decisions that arise in this process include selecting the...

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Bibliographic Details
Published inOperations research Vol. 49; no. 2; pp. 271 - 280
Main Authors Karmarkar, Uday S, Rajaram, Kumar
Format Journal Article
LanguageEnglish
Published Linthicum INFORMS 01.03.2001
Operations Research Society of America
Institute for Operations Research and the Management Sciences
Subjects
Asphalt
Capital costs
Chemical processes
Chemicals
Cost control
Cost functions
Economic costs
Fertilizers
Fixed costs
Glucose
Heuristic
Heuristics
Industrial production
Industries: chemical, food, and petrochemical sectors
Ingredients
Integer programming
Linear programming
Management
Manufacturing
Manufacturing processes
Manufacturing productivity: optimizing cost, and conformance quality
Mathematical programming
Minimization of cost
Operations management
Operations research
Optimal solutions
Optimization
Petrochemicals industry
Processes
Production costs
Production planning
Production planning: blending and grading in process industries
Productivity
Quality control
Software
Specialty chemicals industry
Studies
United States
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Summary:In many chemical process applications, a large mix of products is produced by blending them from a much smaller set of basic grades. The basic grades themselves are typically produced on the same process equipment and inventoried in batches. Decisions that arise in this process include selecting the set of basic grades, determining how much of each basic grade to produce, and how to blend basic grades to meet final product demand. We model this problem as a nonlinear mixed-integer program, which minimizes total grade inclusion, batching, blending, and quality costs subject to meeting quality and demand constraints for these products. Heuristics and lower bounds are developed and tested. The methods are applied to data from Europe's leading manufacturer of wheat- and starch-based products. Our results suggest that this model could potentially reduce annual costs by a minimum of 7%, translates to annual savings of around $5 million.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0030-364X
1526-5463
DOI:10.1287/opre.49.2.271.13533